Impacts of census differential privacy for small-area disease mapping to monitor health inequities.
Yanran LiBrent A CoullNancy KriegerEmily PetersonLance Allyn WallerJarvis T ChenRachel C NetheryPublished in: Science advances (2023)
The U.S. Census Bureau will implement a modernized privacy-preserving disclosure avoidance system (DAS), which includes application of differential privacy, on publicly released 2020 census data. There are concerns that the DAS may bias small-area and demographically stratified population counts, which play a critical role in public health research, serving as denominators in estimation of disease/mortality rates. Using three DAS demonstration products, we quantify errors attributable to reliance on DAS-protected denominators in standard small-area disease mapping models for characterizing health inequities. We conduct simulation studies and real data analyses of inequities in premature mortality at the census tract level in Massachusetts and Georgia. Results show that overall patterns of inequity by racialized group and economic deprivation level are not compromised by the DAS. While early versions of DAS induce errors in mortality rate estimation that are larger for Black than non-Hispanic white populations in Massachusetts, this issue is ameliorated in newer DAS versions.
Keyphrases
- disease activity
- health information
- big data
- systemic lupus erythematosus
- rheumatoid arthritis
- healthcare
- cardiovascular events
- mental health
- public health
- high resolution
- risk factors
- electronic health record
- patient safety
- adverse drug
- machine learning
- cardiovascular disease
- type diabetes
- social media
- coronary artery disease
- artificial intelligence
- high density
- emergency department
- climate change
- quality improvement
- african american